Big data Java vs Python

Each programming language has a different format and structure. Which language we should have to choose when we work with big data or data science. There are basically four programming languages that we can use to work with big data or data science, i.e., Python, Java, R, and Scala. In these four languages, Java and Python are the most commonly used programming languages.

Both languages have certain similarities, so it is difficult to choose a language from both languages. Java and Python are both high-level programming languages, and both follow the OOPs concepts.

Java is the pure form of OOP, but Python is not. Python has a scripting structure. Both are efficient, versatile and mostly used programming languages for mobile apps, big data and other technologies.

To find the answer to the question which language should we use for big data? let's dive into a deep understand of the advantages and disadvantages of both languages and try to understand the fundamental difference between both of them.

Python for Big Data

Python comes with automatic memory management when we use it for big data. It is highly efficient, powerful, and readable language and is used by NASA scientists to program space gadgets. Python has the following features:

1. It is a dynamic language.
2. It is functional and procedural programming.
3. It follows the concepts of OOPs.
4. It supports several programming paradigms.
5. It is scalable.

Beauty, simplicity, clarity, readability and simplicity are the five main goals of Python. In recent years, Python has gained too much popularity because of ML, AI, and Big data technologies. It provides huge libraries for performing the multi-level task. Let's understand the advantages and disadvantages of Python.

Advantages

There are the following advantages of using Python for big data:

1. It is versatile. It is efficient to load, clean, submit, and present data in the form of a website.
2. It is extensible. It provides high-quality libraries like Matplotlib, Numpy, Tensorflow, Pandas and etc. These libraries provide solutions to work with a large dataset.
3. It has an intuitive syntax, which makes it easy to learn.
4. It is stable and predictable in the context of the development cycle.
5. Open source code.
6. Accessible support.
7. It supports the object-oriented programming paradigm.

Disadvantages

Each language comes with both advantages and disadvantages. For using any language to work with big data, we also need to be aware of the possible consequence along with the advantages.

1. Python is an interpreted language, and each line of code runs line by line. It makes Python slow and results in slow execution.
2. It is worst for mobile and browser computing because it is not that secure in this specific niche.
3. There is no need to define the type of variable that can cause runtime errors.

Java for Big Data

Java is the oldest programming language used for Big data technology. It is versatile and incorporates so many data science techniques. The Hadoop platform is completely written in Java to process and store big data applications. It also follows the OOPs concepts and has a C-like syntax that makes it easy to understand. It is mostly used in ETL applications such as:

1. Apache Kafka
2. Apatar
3. Apache Camel

Big data and Java both have some similarities and are synonyms as MapReduce, HDFS, Storm, Kafka, and Scala. Let's understand the pros and cons or advantages and disadvantages of Java. Advantages and Disadvantages play an important role for comparison between any of the languages.

Advantages

There are the following advantages of using Python for Big data:

1. Java is famous for reusability. It has a reusable code.
2. Due to JVM, Java is fast, and its execution is high.
3. It follows the object-oriented programming concepts.
4. It is platform-independent, so we can write code in one machine and can execute it in any of the other machines.
5. It is flexible to add the data science method with an existing code.

Disadvantages

Java has the following disadvantages that restrict us to use it for Big data:

1. It is not suitable to develop analytical applications that are complex and static.
2. Java doesn't provide as much as data science libraries in comparison to R. For the static method, Java has fewer libraries.

Let's understand some differences between both languages that help us to choose the correct language for big data.

Conclusion

In order to choose one language from both of them for Big data depends on our preference and business goals. Both languages have extensive libraries with big communities, support of encapsulation and polymorphism, and an object-oriented approach. Python passes for running the project easily but fails in speed and in the same way Java pas for the speedy execution but fails for running the project easily. Java is best for developing web applications, mobile applications and IoT solutions, and Python is the ease of use in big data, AI, ML and data mining.